The massive scale of Large Language Models (LLMs) incurs prohibitive computational costs, limiting their ubiquitous deployment. While activation sparsity is a promising training-free solution, existing magnitude-based methods overlook weight structures and dependencies. We reveal that standard weight norms fail as importance proxies, identifying instead that weight distribution heavy-tailedness is critical. To this end, we propose Kurtosis-Aware Coupled Sparsity (KACS), a novel training-free framework that introduces a Kurtosis-based metric to explicitly capture and prioritize outlier-rich weight columns. Furthermore, we develop an Interaction-Aware evaluation mechanism that assesses the joint importance of structurally coupled projections (e.g., Gate-Up and Q-K-V), ensuring information retention across interacting pathways. In addition, an adaptive sparsity allocation strategy is employed to distribute the sparsity budget across layers and components. Extensive experiments on Llama and Mistral models demonstrate that KACS consistently outperforms state-of-the-art baselines, retaining over 97% of the original performance at 50% sparsity.

Recently, Large Language Models (LLMs) have shown strong potential in recommendation tasks due to their broad world knowledge and reasoning capabilities. However, applying them to serendipity-oriented recommendation remains challenging, mainly due to a domain gap of LLMs in modeling personalized user behavior and the scarcity of labeled serendipitous interactions. In this paper, we introduce SOLAR (Serendipity-Optimized Language model Aligned for Recommendation), a two-stage framework that addresses these challenges. To alleviate label scarcity, we adopt a weak supervision strategy: a sequential ID-based recommender generates candidate items, which are then reranked by an LLM acting as a preference judge to produce serendipity-aware pseudo-labels. To bridge the domain gap, we propose a domain-adaptive instruction tuning method (SUN) that aligns LLMs with recommendation tasks. Experiments on three real-world datasets show that SOLAR consistently improves both accuracy and serendipity over strong baselines, showing its effectiveness in enabling more diverse, user-centric recommendations. Code and dataset are released at https://github.com/SOLAR2025ARR/SOLAR.

Pretrained Language Models (PLMs) have excelled in various Natural Language Processing tasks, benefiting from large-scale pretraining and self-attention mechanism’s ability to capture long-range dependencies. However, their performance on social media application tasks like rumor detection remains suboptimal. We attribute this to mismatches between pretraining corpora and social texts, inadequate handling of unique social symbols, and pretraining tasks ill-suited for modeling user engagements implicit in propagation structures. To address these issues, we propose a continue pretraining strategy called Post Engagement Prediction (PEP) to infuse information from propagation structures into PLMs. PEP makes models to predict root, branch, and parent relations between posts, capturing interactions of stance and sentiment crucial for rumor detection. We also curate and release large-scale Twitter corpus: TwitterCorpus (269GB text), and two unlabeled claim conversation datasets with propagation structures (UTwitter and UWeibo). Utilizing these resources and PEP strategy, we train a Twitter-tailored PLM called SoLM. Extensive experiments demonstrate PEP significantly boosts rumor detection performance across universal and social media PLMs, even in few-shot scenarios. On benchmark datasets, PEP enhances baseline models by 1.0-3.7% accuracy, even enabling it to outperform current state-of-the-art methods on multiple datasets. SoLM alone, without high-level modules, also achieves competitive results, highlighting the strategy’s effectiveness in learning discriminative post interaction features.